Community Detection Using Modularity Optimization Method For Catchment Classification

Abstract

 A general framework for catchment classification may be helpful for more accurate and efficient modeling of hydrologic systems, as well as to improve communication between hydrology researchers and those in other disciplines. There are plethora numbers of methods applied for catchment classification, but in these years, recent studies are implementing the complex networks concept for classification purposes. The community structure methods which are complex networks-based methods are focus mainly to classify catchments. Hence, the efficiency of complex network ideas, especially using the methods of community structure is examined in this study. Specifically, the modularity optimization method that is one of the community structure methods is applied to classify 218 stream-gauges stations in entire Australia that covers a large variety of hydroclimatic, topographic, geomorphic, soil usage, and climatic parameters. In the present study, the applicability and the efficiency of the community structure concept is validated by the proposed method. The classification of Australian catchments was further assessed with threshold value of 0.8, which resulted formation of nine communities with at least 9 stations in a community which combine to have almost 77% of the total number of stations (165 out of 218). All nine selected communities were also examined in terms of the flow characteristics (i.e. flow mean and flow covariance) and the catchment characteristics (i.e. drainage area, elevation and stream length). The catchment behaviors for each selected communities were also interpreted in terms of distance and correlation relationship, which give some useful insights towards generalization of hydrologic framework.